Methods for modeling of finfet width quantization

ABSTRACT

A method for modeling FinFET width quantization is described. The method includes fitting a FinFET model of a FinFET device to single fin current/voltage characteristics. The FinFET device comprises a plurality of fins. The method includes obtaining statistical data of at least one sample FinFET device. The statistical data includes DIBL data and SS data. The method also includes fitting the FinFET model to a variation in a current to turn off the finFETs device (I OFF ) in the statistical data using the DIBL data and the SS data and determining a model for a voltage to turn off the finFETs device (V OFF ). The method also includes fitting the FinFET model to the V OFF .

TECHNICAL FIELD

The exemplary embodiments of this invention relate generally to fieldeffect transistors (FETs) and, more specifically, relate to modelingFinFET width quantization.

BACKGROUND

This section is intended to provide a background or context. Thedescription herein may include concepts that could be pursued, but arenot necessarily ones that have been previously conceived or pursued.Therefore, unless otherwise indicated herein, what is described in thissection is not prior art to the description and claims in thisapplication and is not admitted to be prior art by inclusion in thissection.

Semiconductors and integrated circuit chips have become ubiquitouswithin many products due to their continually decreasing cost and size.In the microelectronics industry as well as in other industriesinvolving construction of microscopic structures (such as micromachines,magnetoresistive heads, etc.) there is a continued desire to reduce thesize of structural features and microelectronic devices and/or toprovide a greater amount of circuitry for a given chip size.Miniaturization, in general, allows for increased performance (such asmore processing per clock cycle and less heat generated for example) atlower power levels and lower cost. Current technology is at orapproaching atomic level scaling of certain micro-devices such as logicgates, FETs and capacitors. Circuit chips with hundreds of millions ofsuch devices are not uncommon. Further size reductions appear to beapproaching the physical limit of trace lines and micro-devices that areembedded upon and within their semiconductor substrates.

BRIEF SUMMARY

In an exemplary aspect, a method for modeling FinFET width quantizationincludes: fitting a FinFET model of a FinFET device to single fincurrent/voltage characteristics, where the FinFET device comprises aplurality of fins; obtaining statistical data of at least one sampleFinFET device, where the statistical data comprises DIBL data and SSdata; fitting the FinFET model to a variation of offstate current(I_(OFF)) in the statistical data using the DIBL data and the SS data;determining a second-order polynomial model for the turn-off voltage(VOFF) in the compact model. The VOFF only impacts the subthresholdbehaviors but not the on-state behavior of the transistor.

In another exemplary aspect, a method for modeling FinFET widthquantization includes: obtaining statistical data of a FinFET device,where the statistical data comprises DIBL data and SS data, and wherethe FinFET device comprises a plurality of fins; defining a productfitness merit based on the DIBL data and the SS data using a singleparameter statistical model; defining at least one DIBL guardband basedon a product performance metric; defining at least one SS guardbandbased on a product performance metric; and determining a product qualityof the FinFET device based at least in part on the at least one DIBLguardband, the at least one SS guardband and the product fitness merit.

In a further exemplary aspect, an article of manufacture tangiblyembodying computer readable non-transitory instructions which, whenimplemented, cause a computer to carry out the steps of a method formodeling fin field effect transistor (FinFET) width quantization. Themethod includes fitting a FinFET model of a FinFET device to single fincurrent/voltage characteristics, where the FinFET device comprises aplurality of fins; obtaining statistical data of at least one sampleFinFET device, where the statistical data comprises DIBL data and SSdata; fitting the FinFET model to a variation in a current to turn offthe finFETs device (I_(OFF)) in the statistical data using the DIBL dataand the SS data; determining a second-order polynomial model for theturn-off voltage (VOFF) in the compact model. The VOFF only impacts thesubthreshold behaviors but not the on-state behavior of the transistor

In another exemplary aspect, an article of manufacture tangiblyembodying computer readable non-transitory instructions which, whenimplemented, cause a computer to carry out the steps of a method formodeling fin field effect transistor (FinFET) width quantization. Themethod includes: obtaining statistical data of a FinFET device, wherethe statistical data comprises DIBL data and SS data, and where theFinFET device comprises a plurality of fins; defining a product fitnessmerit based on the DIBL data and the SS data using a single parameterstatistical model; defining at least one DIBL guardband based on aproduct performance metric; defining at least one SS guardband based ona product performance metric; and determining a product quality of theFinFET device based at least in part on the at least one DIBL guardband,the at least one SS guardband and the product fitness merit.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The foregoing and other aspects of exemplary embodiments are made moreevident in the following Detailed Description, when read in conjunctionwith the attached Drawing Figures, wherein:

FIGS. 1A, 1B and 1C, collectively referred to as FIG. 1, illustrate theimpact fin count has on statistical variation.

FIG. 2 shows a simplified block diagram of a circuit model usingmultiple discrete compact models.

FIG. 3 illustrates the relationship of fin count to the variation inDIBL voltage threshold.

FIG. 4 illustrates the relationship of fin count to variation in I_(off)and to variation in I_(on).

FIG. 5 demonstrates the DIBL vs. subthreshold slope (SS) of variousdevices with different N_(fin) and variations in D_(fin).

FIG. 6 displays comparisons of gate voltage to drain current.

FIG. 7 shows the results of fitting a second-order polynomial analyticalmodel to statistical data.

FIG. 8 illustrates a complete I_(off) fitting in accordance with anexemplary embodiment.

FIG. 9 demonstrates the DIBL vs. SS of various devices with differentN_(fin) and variations in D_(fin) and also demonstrates various boundarylimits.

FIG. 10 shows a simplified block diagram of an exemplary electronicdevice that is suitable for use in practicing various exemplaryembodiments.

FIG. 11 is a logic flow diagram that illustrates the operation of anexemplary method, and a result of execution of computer programinstructions embodied on a computer readable memory, in accordance withvarious exemplary embodiments.

FIG. 12 is a logic flow diagram that illustrates the operation ofanother exemplary method, and a result of execution of computer programinstructions embodied on a computer readable memory, in accordance withvarious exemplary embodiments.

DETAILED DESCRIPTION

The following abbreviations that may be found in the specificationand/or the drawing figures are defined as follows:

-   -   CMOS complementary metal-oxide semiconductor    -   delvt threshold voltage adjust    -   DIBL drain induced barrier lowering    -   FET field effect transistor    -   FinFET fin-type FET    -   ILT in-line test    -   MOS metal-oxide semiconductor    -   NFET n-type FET    -   PFET p-type FET    -   Rdsw width coefficient of parasitic resistance    -   SOI silicon-on-insulator    -   SRAM static random access memory    -   SS subthreshold slope    -   Wdrawn drawn channel width

A field effect transistor (FET) is a transistor having a source, a gate,and a drain. The action of the FET depends on the flow of majoritycarriers along a channel between the source and drain that runs past thegate. Current through the channel, which is between the source anddrain, may be controlled by a transverse electric field under the gate.

As known to those skilled in the art, P-type FETs (PFETs) turn ON toallow current flow between source and drain when the gate terminal is ata low or negative potential with respect to the source. When the gatepotential is positive or the same as the source, the P-type FET is OFFand does not conduct current. On the other hand, N-type FETs (NFETs)turn ON to allow current flow between source and drain when the gateterminal is high or positive with respect to the source. When the gatepotential is negative or the same as the source, the N-type FET is OFFand does not conduct current. Note that in each of these cases there isa threshold voltage (such as at the gate terminal) for triggeringactuation of the FET.

More than one gate can be used to more effectively control the channel.The length of the gate determines how fast the FET switches, and can beabout the same as the length of the channel (such as the distancebetween the source and drain). Multi-gate FETs are considered to bepromising candidates to scale down complementary metal-oxidesemiconductor (CMOS) FET technology. However, such small dimensionsnecessitate greater control over performance issues such as shortchannel effects, punch-through, metal-oxide semiconductor (MOS) leakagecurrent and the parasitic resistance that is present in a multi-gateFET.

The channel length of FETs has been successfully reduced through the useof one or more fin-shaped channels. A FET employing such a channelstructure may be referred to as a FinFET. Previously, CMOS devices weresubstantially planar along the surface of the semiconductor substrate,the exception being the FET gate that was disposed over the top of thechannel. Fins break from this paradigm by using a vertical channelstructure in order to maximize the surface area of the channel that isexposed to the gate. The gate controls the channel more strongly becauseit extends over more than one side (surface) of the channel. Forexample, the gate can enclose three surfaces of the three-dimensionalchannel, rather than being disposed only across the top surface of thetraditional planar channel.

The nature of FinFET devices prohibits continuous width scaling andintroduces a digitization of device width. As a consequence, devices arecomprised of arrays of fins ranging from one (such as for SRAM) to a fewtens of fins. This introduces an intrinsic variation in the device thatis absent in conventional planar devices.

FIGS. 1A, 1B and 1C, collectively referred to as FIG. 1, illustrate theImpact that fin count has on statistical variation. FIG. 1A shows asimplified block diagram of a multiple finFET device 100. The device 100includes a number of finFETs (N_(fin)), n, which provide a source 110, agate 120 and a drain 130. The width of an individual finFET (W_(1-fin))is approximately twice the height of the fins (H_(fin)) for adouble-gate finFET and twice the height of the Hfin plus the finthickness (Dfin) for a triple-gate finFET (or Trigate). The effectivewidth of the device 100 is n×W_(1-fin).

Statistical variation in a single fin can dominate an electricalresponse (such as I_(off) for example) in low fin count FinFETs (Nfin=6)in contrast to high fin count FinFETs (Nfin=30). FIGS. 1B and 1Cillustrate this effect.

FIG. 1B illustrates the relationship of gate voltage to drain currentwhen there are six finFETs (N_(fin)=6). The graph represents datacollected over a sampling of devices 100. As the curve 140 approaches agate voltage of 0 V, variations in individual finFETs begin to impactthe curve 140.

FIG. 1C illustrates the relationship of gate voltage to drain currentwhen there are thirty finFETs (N_(fin)=30). As in FIG. 1B, the graphrepresents data collected over a sampling of devices 100. As the curve150 approaches a gate voltage of 0 V, variations in individual finFETsalso impact the curve 150; however, due to the larger number of finFETs,this effect is less pronounced.

FIG. 2 shows a simplified block diagram of a circuit model usingmultiple discrete compact models. The circuit 100 includes n individualfinFETs 210. The gates 120 of all finFETs 210 are connected together.Similarly, the drains 130 are connected to each other and the sources110 are connected to each other.

To build a reliable circuit model parameters are used which take intoaccount the scaling behavior caused by increasing the number of fins forthe composite device. Using various exemplary embodiments, composite finbehavior can be modeled correctly using a single fin model. Thestatistical drive current and the leakage current distribution may alsobe accurately modeled. Drain induced barrier lowering (DIBL) is asubthreshold relationship for the composite device which can be used asan easily accessible indicator and calibration aid for the intrinsicvariations observed in a composite device.

Symmetric double-gate FinFETs are promising candidates for 22 nm andbeyond manufacturing due to better short channel behaviors, such assteep subvt slope and low DIBL. The effective electrical width (Weff) ina FinFET technology is quantized depending on Wdrawn values and finpitch. However, differences in fin thickness (D_(fin)) variationintroduce variations in DIBL for a given fin. D_(fin) variation is amajor source of variation in fully-depleted FinFETs with many finsbecause D_(fin) variation in a single fin can significantly dominatecomposite transistor properties such as I_(off).

Technology improvements can reduce fin thickness but circuit designersultimately need efficient and accurate methods for compact modeling forany residual variation. Additionally, rapid testing may be used toassess product quality during a manufacturing inline test (ILT).

Statistical hardware data trends to be modeled may include the voltagethreshold caused by DIBL (V_(th,DIBL)). The number of fins (N_(fin))influences the variation in the voltage threshold caused by DIBL (σ_(V)_(th,DIBL) ) such that regardless of the process used, σ_(V) _(th,DIBL)[N fins] increases as N_(fin) approaches 1 (similar to as seen in FIGS.1B and 1C), as shown by the following equation:

$\begin{matrix}{{\sigma_{V_{{th},{DIBL}}}\left\lbrack {N\mspace{14mu} {fins}} \right\rbrack} = {\frac{\sigma_{V_{{th},{DIBL}}}\left\lbrack {1\mspace{14mu} {fins}} \right\rbrack}{\sqrt{N_{fin}}}.}} & (1)\end{matrix}$

FIG. 3 illustrates the relationship of fin count to the variation inDIBL voltage threshold. The V_(th,DIBL) is compared for two processes:process A, where the variation between individual fin thicknesses(D_(fin)) is large; and process B, where the variation in D_(fin) issmall. As shown, line 320 shows a slower change in the variation in thevoltage threshold is seen as the number of fin decrease when thevariation in D_(fin) is small (process B). In contrast, line 310 shows amuch faster growth in σ_(V) _(th,DIBL) [N fins] when the variation inD_(fin) is large (process A). Thus, when N_(fin) is small any variationin D_(fin) plays a greater role in the variation in the voltagethreshold caused by DIBL. By extension, variations in D_(fin) play aless significant role when there is a larger number of finFETs.

FIG. 4 illustrates the relationship of fin count to variation in currentto turn off the finFETs device (I_(off)) and to variation in current toturn on the finFETs device (I_(on)). As shown, the line 410 shows thevariation in I_(on) for process A (where variation in D_(fin) is large)and line 420 shows the variation in I_(on) for process B. Both lines 410and 420 vary linearly in

$\frac{1}{\sqrt{N_{fin}}}.$

Line 415 shows the variation in I_(off) for process B and line 425 showsthe variation in I_(off) for process A. Because I_(off) of line 415 isdominated by the worst fin (thickest) in the array of fins, I_(off) ofline 415 does not vary exponentially in

$\frac{1}{\sqrt{N_{fin}}}.$

Hardware statistical trends can be modeled for multiple fin devices andcompared with single fin devices. The dispersion in DIBL may be comparedagainst sub-threshold slope (SS) in order to establish a metric of σ_(D)_(fin) variability in multi-fin structures.

FIG. 5 demonstrates the DIBL vs. subthreshold slope (SS) of variousdevices with different variation in D_(fin) (σ_(D) _(fin) ) and N_(fin).Points 530 represent single finFETs devices (N_(fin)=1) with σD_(fin) of1 nm and 2.25 nm. Points 520 represent multiple finFETs devices having20 individual finFETs (N_(fin)=20) with σD_(fin) of 1 nm and points 510represent multiple finFETs devices (where N_(fin)=20) with σD_(fin) of2.25 nm. Area 540 highlights a section of the graph where points 520 arepredominantly located. As shown, points 510 are dispersed widely, whilepoints 520 are located closer to the line described by points 530.

The SS influences the transition between states (either off or on).Thus, multiple finFETs devices with high σD_(fin) (such as thoserepresented by points 510) may experience greater variation intransitions between states then such devices having less variation inD_(fin).

Rather than using a statistical circuit analysis using multiple discretecompact models for individual fins (where each fin has an independentstatistical variation), various exemplary embodiments fit existingcompact model to a nominal single fin I-V characteristics.

The general form of V_(th) variation may be given as follows:

$\begin{matrix}{{{\sigma_{V_{th}}^{2} = {{\sum\limits_{i}^{\;}\; {\left. \left( {\frac{\partial V_{th}}{\partial p_{i}}\sigma_{p_{i}}} \right)^{2} \right.\sim\left( {\frac{\partial V_{th}}{\partial L_{g}}\sigma_{L_{g}}} \right)^{2}}} + \left( {\frac{\partial V_{th}}{\partial H_{fin}}\sigma_{H_{fin}}} \right)^{2} + \left( {\frac{\partial V_{th}}{\partial D_{fin}}\sigma_{D_{fin}}} \right)^{2} + \left( {\frac{\partial V_{th}}{\partial T_{ox}}\sigma_{T_{ox}}} \right)^{2} + \left( {\frac{\partial V_{th}}{\partial N_{ch}}\sigma_{N_{ch}}} \right)^{2} + \left( {\frac{\partial V_{th}}{\partial\text{?}}\text{?}} \right)^{2}}},{\text{?}\text{indicates text missing or illegible when filed}}}\mspace{315mu}} & (2)\end{matrix}$

where p_(i) is a general statistical variable, L_(g) is gate length,H_(fin) is fin height, D_(fin) is fin thickness, T_(ox) is effectivegate dielectric thickness, N_(ch) is channel doping (which is negligiblefor a fully depleted fin), and Φ_(g) is gate work function.

For DIBL, significant inter-fin sources of variation can be writtenusing a Pelgrom-like form:

${\left. \Rightarrow{\sigma_{V_{{th},{lin}}}^{2} - \sigma_{V_{{th},{sat}}}^{2}} \right. = \left. {\left. \sigma_{V_{{th},{DIBL}}}^{2} \right.\sim\left. \frac{1}{{Area}_{channel}} \right.\sim\frac{1}{2\; L_{g}H_{fin}N_{fin}}}\Rightarrow{{\left. \sigma_{V_{{th},{DIBL}}} \right.\sim\frac{constant}{\left. \sqrt{}N_{fin} \right.}}.} \right.}\;$

In a first exemplary embodiment, a compact model (such as a BSIM-CMGmodel, a BSIMSOI model, etc. for example) may be fit to nominal singlefin I-V characteristics. Next statistical data is obtained (such asmeasurement data of hardware or simulation data for example). A singleparameter statistical model (such as an off voltage (V_(OFF))statistical model for example) is used to fit I_(off) variation in thestatistical data based on the relationship between DIBL and SS (as seenin FIG. 1). A model (such as, in a non-limiting example, a second-orderpolynomial model) is generated (such as where

${VOFF} = {A + \frac{B\; 1}{\sqrt{N_{fin}}} + \frac{B\; 2}{N_{fin}}}$

for example). The model is then used to fit V_(OFF) to the statisticaldata.

The first exemplary embodiment may also include fitting I_(on) usingmobility (such as a μ₀ model parameter for example) and/or seriesresistance (such as a rdsw model parameter for example). The exemplaryembodiment may also include fitting gate capacitance (C_(gate)) using aI-V to C-V threshold adjust parameter (such as a delvt parameter forexample).

FIG. 6 displays comparisons of gate voltage vs. drain current forone-fin finFET devices biased at differrnt Vds. Points 610 representdata measured from single finFET devices having a of 0.9 V and points620 measured from single finFET devices having a Vds of 0.05 V. Lines612 and 622 illustrate compact models (such as BDIM-CMG, BSIMSOI, etc.for example) which have been fit to nominal single fin characteristics.Line 612 has been fit to points 610 and line 622 has been fit to points622.

FIG. 7 shows results of fitting a second-order polynomial analyticalmodel to statistical data as performed in the first exemplaryembodiments. Various curves 710, 720, 730, 740 and 750 are shown foranalytical models which are fit to various corners. Curve 730 shows ananalytical model which has been fit to the median deviation.

FIG. 8 illustrates a complete I_(off) fitting in accordance with thefirst exemplary embodiment. Points 810 illustrate results from multiplesimulations. Points 820 illustrate the results from a model generated inaccordance with this invention. Points 820 match closely with the curvedescribed by points 810.

In contrast, points 830 illustrate the results of a conventionalapproach using variation in V_(th) only. While points 830 reflect acurve that is similar to that described by points 820, points 820provide a more accurate Ioff prediction

Reference is made to FIG. 10 for illustrating a simplified block diagramof various electronic devices and apparatus that are suitable for use inpracticing exemplary embodiments. For example, computer 1010 may be usedto control a lithography process in accordance with an exemplaryembodiment.

The computer 1010 includes a controller, such as a computer or a dataprocessor (DP) 1014 and a computer-readable memory medium embodied as amemory (MEM) 1016 that stores a program of computer instructions (PROG)1018.

The PROGs 1018 is assumed to include program instructions that, whenexecuted by the associated DP 1014, enable the device to operate inaccordance with exemplary embodiments, as will be discussed below ingreater detail.

That is, various exemplary embodiments may be implemented at least inpart by computer software executable by the DP 1014 of the computer1010, or by hardware, or by a combination of software and hardware (andfirmware).

The computer 1010 may also include dedicated processors, for exampleFinFET modeling processor 1015.

The computer readable MEM 1016 may be of any type suitable to the localtechnical environment and may be implemented using any suitable datastorage technology, such as semiconductor based memory devices, flashmemory, magnetic memory devices and systems, optical memory devices andsystems, fixed memory and removable memory. The DP 1014 may be of anytype suitable to the local technical environment, and may include one ormore of general purpose computers, special purpose computers,microprocessors, digital signal processors (DSPs) and processors basedon a multicore processor architecture, as non-limiting examples.

The exemplary embodiments, as discussed herein and as particularlydescribed with respect to exemplary methods, may be implemented inconjunction with a program storage device (e.g., at least one memory)readable by a machine, tangibly embodying a program of instructions(e.g., a program or computer program) executable by the machine forperforming operations. The operations comprise steps of utilizing theexemplary embodiments or steps of the method.

Based on the foregoing it should be apparent that various exemplaryembodiments provide a method, apparatus and computer program(s) foraccurate modeling of inter-fin variation of fm thickness variation.

FIG. 11 is a logic flow diagram that illustrates the operation of amethod, and a result of execution of computer program instructions (suchas PROG 1018), in accordance with exemplary embodiments. In accordancewith these exemplary embodiments a method performs, at Block 1110, astep of fitting a FinFET model of a FinFET device to single fincurrent/voltage characteristics. The FinFET device includes a pluralityof fins. The method performs, at Block 1120, a step of obtainingstatistical data of at least one sample FinFET device. The statisticaldata includes DIBL data and SS data. At Block 1130, the method performsa step of fitting the FinFET model to a variation in a current to turnoff the finFETs device (I_(OFF)) in the statistical data using the DIBLdata and the SS data. The method performs, at Block 1140, a step ofdetermining a model for a voltage to turn off the finFETs device(V_(OFF)). The method also performs, at Block 1150, a step of fittingthe FinFET model to the V_(OFF).

As shown in the first exemplary embodiment, the relationship betweenDIBL and SS may be used as a correlating component in a variabilitymodel. This relationship may also be used as a metric of variability.

In another exemplary embodiment, DIBL-SS statistical data is obtainedfor example by inline tests during manufacturing. A single parameterstatistical model (such as V_(OFF) statistical model for example) isused to define a product fitness metric for D_(fin) based on therelationship between DIBL and SS. The V_(OFF) parameter changessubthreshold behavior but not on-state behavior. A first set of guardbands is selected for DIBL based on a product performance metric (suchas a ring oscillator delay for example). A second set of guard bands isselected for SS based on a product power metric (such as leakage currentfor example). The sets of guard bands may then be used to screen theDIBL-SS statistical data. Based on the screening, an analysis of productquality may be provided (for example, where values falling within theguard bands are deemed acceptable).

The guard bands may be set based on past measured data, simulations(such as TCAD, SPICE, or mixed mode TCAD/SPICE for example), or by auser (such as based on extrapolation from past technology nodes). Somefactors for setting guard bands include basic device design parameters(such as nominal Fin thickness and other physical design specificationsfor example) and the expected device electrical performancespecifications.

In a further exemplary embodiment, addition parameters (such as gatelength for example) may be selected. These parameters may then be usedfor further screening the DIBL-SS statistical data by filtering theDIBL-SS statistical data at targeted gate length.

FIG. 9 demonstrates the DIBL vs. subthreshold slope (SS) of variousdevices with different variation in D_(fin) (σD_(fin)) and N_(fin) asshown in FIG. 5. Additionally, FIG. 9 demonstrates various boundaryconditions. Limits may be placed on the DIBL (910, 915) and on the SS(920, 925) in order to define in-line test results which would beconsidered passing (that the device specification is acceptable).

FIG. 12 is a logic flow diagram that illustrates the operation of amethod, and a result of execution of computer program instructions (suchas PROG 1018), in accordance with exemplary embodiments. In accordancewith these exemplary embodiments a method performs, at Block 1210, astep of obtaining statistical data of a FinFET device. The statisticaldata includes DIBL data and SS data. The FinFET device includes aplurality of fins. The method performs, at Block 1220, a step ofdefining a product fitness merit based on the DIBL data and the SS datausing a single parameter statistical model. At Block 1230, the methodperforms a step of defining at least one DIBL guardband based on aproduct performance metric, and, at Block 1240, the method performs astep of defining at least one SS guardband based on a productperformance metric. The method also performs, ay Block 1250, a step ofdetermining a product quality of the FinFET device based at least inpart on the at least one DIBL guardband, the at least one SS guardbandand the product fitness merit.

The various blocks shown in FIGS. 11 and 12 may be viewed as methodsteps, and/or as operations that result from operation of computerprogram code, and/or as a plurality of coupled logic circuit elementsconstructed to carry out the associated function(s).

Any use of the terms “connected”, “coupled” or variants thereof shouldbe interpreted to indicate any such connection or coupling, direct orindirect, between the identified elements. As a non-limiting example,one or more intermediate elements may be present between the “coupled”elements. The connection or coupling between the identified elements maybe. as non-limiting examples, physical, electrical, magnetic, logical orany suitable combination thereof in accordance with the describedexemplary embodiments. As non-limiting examples, the connection orcoupling may comprise one or more printed electrical connections, wires,cables, mediums or any suitable combination thereof.

Generally, various exemplary embodiments can be implemented in differentmediums, such as software, hardware, logic, special purpose circuits orany combination thereof. As a non-limiting example, some aspects may beimplemented in software which may be run on a computing device, whileother aspects may be implemented in hardware.

The foregoing description has provided by way of exemplary andnon-limiting examples a full and informative description of the bestmethod and apparatus presently contemplated by the inventors forcarrying out various exemplary embodiment. However, variousmodifications and adaptations may become apparent to those skilled inthe relevant arts in view of the foregoing description, when read inconjunction with the accompanying drawings and the appended claims.However, all such and similar modifications will still fall within thescope of the teachings of the exemplary embodiments.

Furthermore, some of the features of the preferred embodiments could beused to advantage without the corresponding use of other features. Assuch, the foregoing description should be considered as merelyillustrative of the principles, and not in limitation thereof.

1. A method for modeling fin field effect transistor (FinFET) widthquantization, comprising: fitting by using a data processor points of acurve representing a FinFET model of a FinFET device to points of acurve representing single fin current and voltage characteristics, wherethe FinFET device comprises a plurality of fins; obtaining statisticaldata of sample FinFET devices, where the statistical data comprisesdrain induced barrier lowering (DIBL) data and subthreshold slope data;and fitting the FinFET model to a variation in a current to turn off theFinFET devices (I_(off)) in the statistical data using the DIBL data andthe subthreshold slope data, where fitting the FinFET model comprisesdetermining a model for a FinFET devices turn-off voltage parameter,V_(OFF), where the V_(OFF) parameter changes subthreshold behavior butnot on-state behavior.
 2. The method of claim 1, where the statisticaldata is one of: measured data and simulation data.
 3. A method formodeling fin field effect transistor (FinFET) width quantization, themethod being performed at least in part by execution of computer programinstructions by at least one computer, comprising: fitting by using adata processor points of a curve representing a FinFET model of a FinFETdevice to points of a curve representing single fin current and voltagecharacteristics, where the FinFET device comprises a plurality of fins;obtaining statistical data of sample FinFET devices, where thestatistical data comprises drain induced barrier lowering (DIBL) dataand subthreshold slope data; and fitting the FinFET model to a variationin a current to turn off the FinFET devices (I_(off)) in the statisticaldata using the DIBL data and the subthreshold slope data, where fittingthe FinFET model comprises determining a model for a FinFET devicesturn-off voltage parameter, V_(OFF), where the V_(OFF) parameter changessubthreshold behavior but not on-state behavior, and where the model forthe turn-off parameter satisfies the equation:${V_{OFF} = {A - \frac{B\; 1}{\sqrt{N_{fin}}} + \frac{B\; 2}{N_{fin}}}},$where A, B1 and B2 are constants, and N_(fin) is a number of individualfins in the FinFET device.
 4. The method of claim 1, further comprisingfitting a current to turn on the finFETs device (I_(on)) using at leastone of: a mobility parameter and a series resistance parameter.
 5. Themethod of claim 1, further comprising fitting a gate capacitance(C_(gate)) using a current-voltage (I-V) to capacitance-voltage (C-V)threshold change parameter. 6-20. (canceled)
 21. An apparatus comprisingat least one data processor configured to perform operations comprising:to fit points of a curve representing a FinFET model of a FinFET deviceto points of a curve representing single fin current and voltagecharacteristics, where the FinFET device comprises a plurality of fins;to obtain statistical data of sample FinFET devices, where thestatistical data comprises drain induced barrier lowering (DIBL) dataand subthreshold slope data; and to fit the FinFET model to a variationin a current to turn off the FinFET devices (I_(off)) in the statisticaldata using the DIBL data and the subthreshold slope data, where theFinFET model is fit to the variation in current by determining a modelfor a FinFET devices turn-off voltage parameter, V_(OFF), where theV_(OFF) parameter changes subthreshold behavior but not on-statebehavior.
 22. The apparatus of claim 21, where the statistical data isone of: measured data and simulation data.
 23. An apparatus comprisingat least one data processor configured to perform operations comprising:to fit points of a curve representing a FinFET model of a FinFET deviceto points of a curve representing single fin current and voltagecharacteristics, where the FinFET device comprises a plurality of fins:to obtain statistical data of sample FinFET devices, where thestatistical data comprises drain induced barrier lowering (DIBL) dataand subthreshold slope data; and to fit the FinFET model to a variationin a current to turn off the FinFET devices (I_(off)) in the statisticaldata using the DIBL data and the subthreshold slope data, where theFinFET model is fit to the variation in current by determining a modelfor a FinFET devices turn-off voltage parameter, V_(OFF), where theV_(OFF) parameter changes subthreshold behavior but not on-statebehavior; and where the model for the turn-off parameter satisfies theequation:${V_{OFF} = {A + \frac{B\; 1}{\sqrt{N_{fin}}} + \frac{B\; 2}{N_{fin}}}},$where A, B1 and B2 are constants, and N_(fin) is a number of individualfins in the FinFET device.
 24. The apparatus of claim 21, furthercomprising to fit a current to turn on the finFETs device (I_(on)) usingat least one of: a mobility parameter and a series resistance parameter.25. The apparatus of claim 21, where at least one data processor isfurther configured to fit a gate capacitance (C_(gate)) using acurrent-voltage (I-V) to capacitance-voltage (C-V) threshold changeparameter. 26-28. (canceled)
 29. The method of claim 1, where the methodis performed at least in part by execution of computer programinstructions by the data processor, and where the computer programinstructions are stored on a non-transitory memory medium that isreadable by the data processor.
 30. The method of claim 3, where thecomputer program instructions are stored on a non-transitory memorymedium that is readable by the at least one computer.